first commit

app.py

@@ -0,0 +1,62 @@
+import os
+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# Define path the model
+PATH_PROTOTXT   = os.path.join('saved_model/MobileNetSSD_deploy.prototxt')
+PATH_MODEL      = os.path.join('saved_model/MobileNetSSD_deploy.caffemodel')
+# Define clasess model
+CLASSES = [
+	'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
+	'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'hourse',
+	'motorbike', 'person', 'porredplant', 'sheep', 'sofa', 'train', 'tvmonitor'
+]
+
+# Load model
+NET = cv2.dnn.readNetFromCaffe(PATH_PROTOTXT, PATH_MODEL)
+
+def person_counting(image, threshold=0.7):
+    '''
+    Counting the number of people in the image
+    Args:
+        image: image to be processed
+        threshold: threshold to filter out the objects
+    Returns:
+        image: image with rectangles people detected
+        counting: count of people
+    '''
+    
+    counting = 0
+    W, H = image.shape[1], image.shape[0]
+    blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)
+    NET.setInput(blob); detections = NET.forward()
+
+    for i in np.arange(0, detections.shape[2]):
+        conf = detections[0, 0, i, 2]
+        idx = int(detections[0, 0, i, 1])
+        if CLASSES[idx] == 'person' and conf > threshold:
+            box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
+            x_min, y_min, x_max, y_max = box.astype('int')
+            counting += 1
+            cv2.rectangle(image, pt1=(x_min,y_min), pt2=(x_max,y_max), color=(255,0,0), thickness=1)
+    return image, counting
+
+title = 'People counting'
+css = ".image-preview {height: auto !important;}"
+
+inputs = [gr.inputs.Image(source='upload'), gr.Slider(0, 1, value=0.5, label='threshold')]
+outputs = [gr.outputs.Image(label='image output'), gr.Number(label='counting')]
+examples = [[f'images/{i}', 0.5] for i in os.listdir('images')]
+
+iface = gr.Interface(
+    title   = title,
+    fn      = person_counting, 
+    inputs  = inputs, 
+    outputs = outputs,
+    examples= examples,
+    css=css
+)
+
+iface.launch()